Diffractometer-based global in situ diagnostic system

ABSTRACT

An in vivo human-tissue analysis and communication system produces a quantitative diagnostic indicator for human-tissue analyzed by the system. The system includes a human-tissue-analyzer subsystem with at least one human-tissue analyzer constructed to analyze human tissue and to produce a quantitative-diagnostic indicator. There is also a two-way communication subsystem constructed to allow the human-tissue-analyzer subsystem to send and receive information relevant to the quantitative-diagnostic indicator. The human-tissue-analyzer subsystem includes at least one tissue diffractometer operatively coupled to a computer database over a network, and configured for acquisition of human-tissue data and transfer to the computer database over the network. A computer processor is operatively coupled to the tissue diffractometer, and the computer processor is configured to receive the human-tissue data from the tissue diffractometer, transmit the human-tissue data to the computer database; and process the human-tissue data using a data analytics algorithm that provides a quantitative-diagnostic indicator of human tissue.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.17/593,846, filed Sep. 26, 2021 and entitled “Diffractometer-BasedGlobal In Situ Diagnostic System”, which application is a U.S. nationalphase application of International Application No. PCT/US2021/037224,filed Jun. 14, 2021, which in turn claims priority to U.S. ProvisionalPatent Application Ser. No. 63,039,345, filed Jun. 15, 2020, thedisclosures which are incorporated herein by reference.

BACKGROUND

Early detection of cancer, e.g., breast cancer, may be correlated withincreased survival rates. Absorptive imaging-based mammographytechniques are widely used as diagnostic tools for detecting thepresence of breast cancer but can suffer from poor contrast and otherfactors that can increase the difficulty of diagnosis based on themammograms.

SUMMARY

An in vivo human-tissue analysis and communication system produces aquantitative diagnostic indicator for human-tissue analyzed by thesystem. The system includes a human-tissue-analyzer subsystem with atleast one human-tissue analyzer constructed to analyze human tissue andto produce a quantitative-diagnostic indicator. There is also a two-waycommunication subsystem constructed to allow the human-tissue-analyzersubsystem to send and receive information relevant to thequantitative-diagnostic indicator. The human-tissue-analyzer subsystemincludes at least one tissue diffractometer operatively coupled to acomputer database over a network, and configured for acquisition ofhuman-tissue data and transfer to the computer database over thenetwork. A computer processor is operatively coupled to the tissuediffractometer, and the computer processor is configured to receive thehuman-tissue data from the tissue diffractometer, transmit thehuman-tissue data to the computer database; and process the human-tissuedata using a data analytics algorithm that provides aquantitative-diagnostic indicator of human tissue.

Additional aspects and advantages of the disclosed concepts will becomereadily apparent to those skilled in the art upon review of thefollowing detailed description, wherein only illustrative embodiments ofthe disclosed concepts are shown and described. As will be realized, theconcepts of the present disclosure may be implemented in other anddifferent embodiments, and the several details thereof are amenable tomodification in various obvious respects, all without departing from thescope of the disclosure. Accordingly, the drawings and description areto be regarded as illustrative in nature, and not as restrictive.

INCORPORATION BY REFERENCE

All publications, patents, and patent applications mentioned in thisspecification are herein incorporated by reference to the same extent asif each individual publication, patent, or patent application wasspecifically and individually indicated to be incorporated by reference.To the extent publications and patents or patent applicationsincorporated by reference contradict the disclosure contained in thespecification, the specification is intended to supersede and/or takeprecedence over any such contradictory material.

BRIEF DESCRIPTION OF THE DRAWINGS

The novel features of the invention are set forth with particularity inthe appended claims. A better understanding of the features andadvantages of the present invention will be obtained by reference to thefollowing detailed description that sets forth illustrative embodiments,in which the principles of the invention are utilized, and theaccompanying drawings (also “Figure” and “FIG.” herein), of which:

FIG. 1 shows an example of a multi beam x-ray diffraction system (fromKomardin, et al., U.S. Pat. No. 6,175,117 B1).

FIG. 2A shows an example of a disordered molecule and an orderedmolecule.

FIG. 2B shows an example of the relative scales of various biologicalobjects (from “Small Angle X-Ray Scattering as a Diagnostic Tool forBreast Cancer”, Sabeena Sidhu, BSc, MSc School of Physics, MonashUniversity, Feb. 12, 2009).

FIGS. 3A-3B show examples of collagen in normal tissue (FIG. 3A) and ininvasive carcinoma tissue (FIG. 3B) (from: “Small Angle X-Ray Scatteringas a Diagnostic Tool for Breast Cancer”, Sabeena Sidhu, BSc, MSc Schoolof Physics, Monash University, Feb. 12, 2009).

FIG. 4 shows examples of x-ray diffraction data generation.

FIGS. 5A-5B show examples of small angle x-ray (SAXS) diffraction data.FIG. 5A: a SAXS pattern from a normal breast tissue biopsy. FIG. 5B: aSAXS pattern from a biopsy of diseased breast tissue.

FIGS. 6A-6B show examples of small angle x-ray diffraction data from abreast tissue with a known disease. FIG. 6A: a SAXS pattern from abiopsy of diseased breast tissue. FIG. 6B: a plot of scatteringintensity as a function of q value.

FIG. 7 shows an example of data extracted from x-ray diffractionpatterns that has been plotted to illustrate the clustering fordifferent tissue types (from: “Small Angle X-Ray Scattering as aDiagnostic Tool for Breast Cancer”, Sabeena Sidhu, BSc, MSc School ofPhysics, Monash University, Feb. 12, 2009).

FIG. 8 shows an illustrative example of a combined mammography and x-raydiffraction instrument.

FIG. 9 shows an example top view of a breast being probed by an x-raydiffraction system.

FIG. 10 shows a schematic of a plurality of tissue diffractometersoperatively coupled to a computer database over a network.

FIG. 11 shows an example schematic for a data collection and processingworkflow.

FIG. 12 shows a computer system that is programmed or otherwiseconfigured to implement methods provided herein.

DETAILED DESCRIPTION

Various embodiments of the invention have been shown and describedherein for example purposes only. Numerous variations, changes, andsubstitutions are possible without departing from the invention. Itshould be understood that various alternatives to the embodiments of theinvention described herein may be employed.

Systems and methods for providing a quantitative diagnostic indicatorfor a subject are disclosed. The systems may comprise a plurality oftissue diffractometers operably coupled to a computer database over anetwork, where the tissue diffractometers are configured to acquiresmall-angle x-ray scattering and/or wide-angle x-ray scattering data fora tissue within the subject. Optionally, the tissue diffractometers mayalso be configured to acquire absorptive images, e.g., mammographyimages, of the tissue. The system is configured to collect and processdiffraction data, image data, and/or other data pertinent to the subjectusing a data analytics algorithm to provide a quantitative diagnosticindicator for the subject. The data analytics algorithm is randomly,periodically, or continually updated and refined using the data for aplurality of subjects stored in the computer database. In someinstances, the quantitative diagnostic indicator may comprise anindicator of the likelihood that the subject has cancer or some otherdisease. In some instances, the quantitative diagnostic indicator maycomprise a diagnosis that the subject has cancer or some other disease.

Whenever the phrase “at least,” “greater than,” or “greater than orequal to” precedes the first numerical value in a series of two or morenumerical values, the phrase “at least,” “greater than” or “greater thanor equal to” applies to each of the numerical values in that series ofnumerical values. For example, greater than or equal to 1, 2, or 3 isequivalent to greater than or equal to 1, greater than or equal to 2, orgreater than or equal to 3.

Whenever the phrase “no more than,” “less than,” or “less than or equalto” precedes the first numerical value in a series of two or morenumerical values, the phrase “no more than,” “less than,” or “less thanor equal to” applies to each of the numerical values in that series ofnumerical values. For example, less than or equal to 3, 2, or 1 isequivalent to less than or equal to 3, less than or equal to 2, or lessthan or equal to 1.

Unless otherwise defined, all of the terms and phrases used herein havethe same meaning as commonly understood by a PHOSITA, a person havingordinary skill in the art that applies to this disclosure.

As used in this specification and the appended claims, the singularforms “a”, “an”, and “the” include plural references unless the contextclearly dictates otherwise. Any reference to “or” herein is intended toencompass “and/or” unless otherwise stated.

As used herein, the phrase “tissue diffractometer” generally refers to adiffractometer configured to record diffraction data from one or moretissues. The tissue diffractometer may be an x-ray diffractometer. Insome instances, the tissue diffractometer may be configured to recorddiffraction data and image data, e.g., mammograms.

As used herein, the phrase “quantitative-diagnostic indicator” refers toan indicator comprising quantitative-diagnostic information that may begenerated with the help of one or more computer processors. The term“quantitative” has the meaning commonly understood by a PHOSITA. Toclarify one aspect of that meaning, “quantitative” as used herein meansthat the corresponding diagnostic indicator is information that can beunderstood by anyone, without the need for professional interpretationby a health professional such as a medical doctor. In some instances,the “quantitative diagnostic indicator” may comprise a probability scorefor the likelihood that a subject has cancer, e.g., breast cancer. Insome instances, the “quantitative diagnostic indicator” may comprise adiagnosis that a subject has cancer, e.g., breast cancer.

As used herein, the term “subject” generally refers to an animal, suchas a mammal. A subject may be a human or non-human mammal. A subject maybe afflicted with a disease or suspected of being afflicted with orhaving a disease. The subject may not be suspected of being afflictedwith or having the disease. The subject may be symptomatic.Alternatively, the subject may be asymptomatic. In some cases, thesubject may be treated to alleviate the symptoms of the disease or curethe subject of the disease. A subject may be a patient undergoingtreatment by a healthcare provider.

As used herein, the phrase “healthcare provider” generally refers to amedical practitioner or support staff. The healthcare provider may be adoctor, a nurse, a dentist, a technician, a student, or the like. Thehealthcare provider may be at least partially responsible for thehealthcare of the subject.

As used herein, the phrase “institution” generally refers to an entityrelated to one or more healthcare providers. The institution may be amedical center, a doctor's office, a clinic, a hospital, a university,or the like.

As used herein, the term “cancer” generally refers to a proliferativedisorder caused or characterized by a proliferation of cells which havelost susceptibility to normal growth control. Cancers of the same tissuetype usually originate in the same tissue and may be divided intodifferent subtypes based on their biological characteristics.Non-limiting examples of categories of cancer are carcinoma (epithelialcell derived), sarcoma (connective tissue or mesodermal derived),leukemia (blood-forming tissue derived) and lymphoma (lymph tissuederived). Cancer may involve every organ and tissue of the body.Specific examples of cancers that do not limit the definition of cancermay include melanoma, leukemia, astrocytoma, glioblastoma,retinoblastoma, lymphoma, glioma, Hodgkin's lymphoma, and chroniclymphocytic leukemia. Examples of organs and tissues that may beaffected by various cancers include pancreas, breast, thyroid, ovary,uterus, testis, prostate, pituitary gland, adrenal gland, kidney,stomach, esophagus, rectum, small intestine, colon, liver, gall bladder,head and neck, tongue, mouth, eye and orbit, bone, joints, brain,nervous system, skin, blood, nasopharyngeal tissue, lung, larynx,urinary tract, cervix, vagina, exocrine glands, and endocrine glands. Insome cases, a cancer can be multi-centric. In some cases, a cancer canbe a cancer of unknown primary (CUP).

As used herein, the term “cloud” generally refers to shared or sharablestorage of electronic data, e.g., a distributed network of computerservers. In some instances, the cloud may be used for archivingelectronic data, sharing electronic data, and analyzing electronic data.

The methods and systems described herein are applied to characterizationof tissues, e.g., soft tissues, within a subject, e.g., characterizationof tissues in situ or in vivo. Examples of organs and tissues that maybe characterized using the disclosed methods and systems include, butare not limited to, pancreas, breast, thyroid, ovary, uterus, testis,prostate, pituitary gland, adrenal gland, kidney, stomach, esophagus,rectum, small intestine, colon, liver, gall bladder, head and neck,tongue, mouth, eye and orbit, bone, joints, brain, nervous system, skin,blood, nasopharyngeal tissue, lung, larynx, urinary tract, cervix,vagina, exocrine glands, and endocrine glands.

Diffractometer-based systems and methods of use: In one aspect, thepresent disclosure provides a system that outputs a quantitativediagnostic indicator for a subject that may have or may be at risk fordeveloping a disease, such as a proliferative disease or cancer. Thesystem may comprise one or more tissue diffractometers operativelycoupled to a computer database over a network. A tissue diffractometerof the one or more tissue diffractometers may be configured for transferof image data, diffraction pattern data, subject data, or anycombination thereof to the computer database over the network. Thesystem may comprise one or more computer processors operatively coupledto the one or more diffractometers. The one or more computer processorsmay be individually or collectively configured to (i) receive the imagedata, diffraction pattern data, subject data, or any combination thereoffrom the one or more tissue diffractometers; (ii) transmit the imagedata, diffraction pattern data, subject data, or any combination thereofto the computer database; and (iii) process the image data, diffractionpattern data, subject data, or any combination thereof for a humansubject using a data analytics algorithm that provides a quantitativediagnostic indicator for the human subject.

In another aspect, the present disclosure provides a method forgenerating a quantitative diagnostic indicator for a subject that mayhave or may be at risk for developing a disease, such as a proliferativedisease or cancer. The method may comprise acquiring data comprisingimage data, diffraction pattern data, subject data, or any combinationthereof for a human subject using one of a plurality of tissuediffractometers operatively coupled to a computer database over anetwork. The plurality of tissue diffractometers may be configured fortransfer of the data to the computer database over the network. One ormore computer processors may be operatively coupled to the plurality oftissue diffractometers. The one or more computer processors may be usedto receive the data comprising image data, diffraction pattern data,subject data, or any combination thereof from the plurality of tissuediffractometers that are operatively coupled to a computer database overthe network and may be configured for transfer of the data comprisingthe image data, diffraction pattern data, subject data, or anycombination thereof to the computer database over the network. The datacomprising the image data, diffraction pattern data, subject data, orany combination thereof may be transmitted to the computer database. Thedata comprising the image data, diffraction pattern data, subject data,or any combination thereof may be processed for the human subject usinga data analytics algorithm that may provide a quantitative diagnosticindicator for the human subject. The following description may relate toboth the method and the system.

In some instances, the one or more tissue diffractometers may be tissuediffractometers as described elsewhere herein. The one or more tissuediffractometers may be stand-alone tissue diffractometers (e.g.,instruments or components of a system that do not comprise otherfunctionalities). The one or more tissue diffractometers may be coupledwith other instruments (e.g., the tissue diffractometer can be attachedto or integrated with an absorption-based mammography imaginginstrument). The operative coupling to a computer database may be over alocal network (e.g., a local area network (LAN)) or a remote network(e.g., the internet).

In some instances, the image data may be images, image metadata, or thelike, or any combination thereof. The images may be raw images (e.g.,images as captured from a detector), processed images (e.g., images thathave had one or more processing operations performed), image analogues(e.g., matrices of intensity values corresponding to pixels, vectorrepresentations of images), or the like. The image metadata may comprisenon-image information regarding the conditions at which the image wasacquired (e.g., x-ray wavelength, detector distance from the sourceand/or the sample, exposure time, date and time of acquisition, ambientconditions, etc.)

In some instances, the diffraction pattern data may comprise diffractionpatterns, diffraction pattern metadata, or the like, or any combinationthereof. The diffraction patterns may comprise diffraction patternsgenerated from an interaction of a radiation beam (e.g., an x-ray beam,a neutron beam) with a tissue. The diffraction patterns may comprise rawdiffraction patterns, processed diffraction patterns, diffractionpattern analogues, or the like. The diffraction pattern metadata maycomprise metadata as described elsewhere herein.

In some instances, the image data and/or diffraction pattern data maycomprise data taken from both a healthy tissue and a tissue suspected ofhaving an abnormality. Both the healthy tissue and the tissue suspectedof having an abnormality may be of a same subject. For example,diffraction data can be taken from both a subject's breast suspected ofhaving a cancer as well as the subject's other breast that is suspectedof being free from the cancer.

In some instances, the human, also referred to herein in as subject,data may comprise an individual subject's age, sex, ancestry data,genetic data, behavioral data, medical history, previous medical testsor diagnostics, occupational data, social determinants of health, or anycombination thereof. The ancestry data may be determined by one or moregenetic tests. The ancestry data may comprise ancestry data reported bythe subject. The genetic data may comprise genetic abnormalities,predispositions, or the like. For example, the subject data may compriseinformation regarding a subject's genetic predisposition to breastcancer (e.g., the presence or absence of a breast cancer gene).

In some instances, the computer database may be a cloud-based database,e.g., a database that resides on one more remote computer servers. Insome instances, the computer database may be a local computer database(e.g., a computer connected to a tissue diffractometer).

In some instances, the one or more computer processors may be computerprocessors that are part of one or more computer servers that host thecomputer database. In some instances, the one or more computerprocessors may be computers operatively coupled to the one or moretissue diffractometers (e.g., computers controlling the one or morediffractometers). The receiving of image data may comprise real-time orsubstantially real-time receipt of the image data. For example, in someinstances, a stream of image data can be transmitted from a tissuediffractometer to the one or more computer processors as the images arebeing taken. In some instances, the image data may be transmitted inpackets (e.g., bundles of one or more images). For example, a series ofimages of a plurality of subjects can be taken throughout a day and canthen be all transmitted together. In another example, all images takenof a single subject during a single scan or single session can betransmitted together. The transmitting to the computer database may bereal-time transmitting, substantially real-time transmitting,intermittent transmitting (e.g., transmitting packets), or anycombination thereof.

In some instances, diffraction data processing and/or image dataprocessing may occur between the receiving of the diffraction and/orimage data and the transmitting of image data. For example, in someinstances, the one or more computer processors may be configured tocompress the diffraction and/or image data to improve the transfer speedto the database. In another example, the one or more computer processorscan be configured to extract relevant parameters (e.g., d spacings, pairdistribution functions) from the data (e.g., diffraction pattern data)before transmitting to the computer database, thereby significantlydecreasing the amount of data to be transmitted. In some instances, theprocessing of diffraction and/or image data may be performed after thedata has been transferred to the computer database. The processing ofthe diffraction and/or image data may be local processing (e.g.,processing on a computer local to the tissue diffractometers) or remoteprocessing (e.g., processing on a remote computer server or cloud-basedserver). In some instances, the data processing may comprise theapplication of a statistical analysis and/or machine learning algorithm(which individually or collectively may be referred to as a “dataanalytics algorithm” herein). The data processing may compriseprocessing diffraction data and/or image data for a single subject or aplurality of subjects. For example, the diffraction and/or image dataacquired for a single subject can be processed to generate thequantitative diagnostic indicator for the subject. In another example,diffraction data and/or image data from a plurality of subjects may beprocessed to refine the data analytics algorithm and/or to generate abaseline diagnostic indicator.

In some instances, the system may further comprise a user interface. Theuser interface may be configured to allow an individual subject and/ortheir healthcare provider to upload the individual subject's image data,diffraction pattern data, subject data, or any combination thereof tothe computer database. The uploading the individual subject's imagedata, diffraction pattern data, subject data, or any combination thereofto the computer database may be in exchange for processing theindividual subject's image data, diffraction patter data, or anycombination thereof to receive the quantitative diagnostic indicator forthe individual subject. For example, a healthcare provider can use theuser interface to upload diffraction images of a suspicious massidentified in a mammogram, along with the absorption-based mammographyimages, to the computer database. In this example, the system comprisingthe one or more computer processors and the computer database can thenprocess the diffraction images, as well as the absorption-basedmammography images, using a data analytics algorithm to generate adiagnostic indicator that is provided to the healthcare provider. Insome instances, the diffraction images and the absorption-basedmammography images may be retained on the computer database, where theycan be used to refine the data analytics algorithm that generates thediagnostic indicator. The user interface may be configured to allow anindividual subject and/or their healthcare provider to make paymentsand/or upload the individual subject's signed consent form. The paymentsmay be cash payments (e.g., the user interface displays an address tosend the payments), check payments (e.g., paper or electronic checkpayments), card payments (e.g., credit or debit card paymentprocessing), app-based payments (e.g., PayPal®, Venmo®), cryptocurrencypayments (e.g., Bitcoin), or any combination thereof. For example, insome instances, an individual subject may pay via a health savingsaccount debit card. The signed consent form may be signed by theindividual subject and/or the healthcare provider. The signed consentform may be related to the quantitative diagnostic indicator. Forexample, the individual subject can sign and upload a consent formstating that the subject's diffraction and/or image data may be retainedon the computer database. In some instances, the signed consent form maybe physically signed, electronically signed, or any combination thereof.

In some instances, a system of the present disclosure may comprise 1, 2,3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 150, 200,250, 300, 350, 400, 450, 500, or more than 500 tissue diffractometers.In some instances, the number of tissue diffractometers in the systemmay range between any two of the values specified in this paragraph. Forexample, in some instances, the number of tissue diffractometers in thesystem may range from 4 to 100. Those of skill in the art will recognizethat in some instances, the number of tissue diffractometers in thesystem may have any value within the range of values specified in thisparagraph, e.g., 125 tissue diffractometers.

The one or more tissue diffractometers may be two or more tissuediffractometers located in two or more different geographic locations.For example, a first tissue diffractometer in a first location can sendone set of image data to the one or more computer processors while asecond tissue diffractometer in a second location can send one set ofdiffraction pattern data to the one or more computer processors. In thisexample, the image data and the diffraction pattern data can both beused to refine the data analytics algorithm that generates quantitativediagnostic indicators for individual subjects, and may also both beretained on the computer database. The one or more tissuediffractometers may comprise a data encryption device. The dataencryption device may comprise a global positioning system (GPS)positioning sensor. The data encryption device may generate encryptedimage data, diffraction pattern data, subject data, or any combinationthereof. The encrypted image data, diffraction pattern data, subjectdata, or any combination thereof may be transferred to the computerdatabase. The encrypted image data, diffraction pattern data, subjectdata, or any combination thereof may comprise data regarding changes ina location of the one or more tissue diffractometers. For example, theimage metadata generated by a tissue diffractometer can compriselocation information for that tissue diffractometer. In this example, amovement of the tissue diffractometer can be tracked using the imagemetadata transmitted by the tissue diffractometer. In another example,the GPS positioning sensor can be in constant communication with thecomputer database regarding the location of the tissue diffractometer.The inclusion of the GPS sensor may reduce a likelihood that the tissuediffractometer is stolen or misappropriated by untrained users. The dataencryption device may be configured to encrypt the data in line with ahealth data privacy standard. For example, the encryption device maymake the transmission and storage of the image data, diffraction patterndata, subject data, or any combination thereof compliant with the HealthInsurance Portability and Accountability Act (HIPAA). The dataencryption device may comprise a module configured to only permitcommunication between the tissue diffractometer and the computerdatabase. For example, other network communications can be disabled suchthat the data from the tissue diffractometer can be sent only to thecomputer database.

In some instances, the plurality of tissue diffractometers that areoperatively coupled to the system may be located in 2, 3, 4, 5, 6, 7, 8,9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 150, 200, 250, 300, 350,400, 450, 500, or more than 500 different geographical locations(thereby effectively constituting a global diagnostics system). In someinstances, the number of different geographical locations comprisingtissue diffractometers that are operatively coupled to the system mayrange between any two of the values specified in this paragraph. Forexample, in some instances, the number of different geographicallocations included in the system may range from 8 to 20. Those of skillin the art will recognize that in some instances, the number ofdifferent geographical locations included in the system may have anyvalue within the range of values specified in this paragraph, e.g., 14different geographical locations.

Small angle x-ray scattering: In some instances, the one or more tissuediffractometers may be configured to perform small angle x-rayscattering (SAXS) measurements. The SAXS measurements may comprisemeasurements of the long-range ordering of the tissue. For example, theSAXS measurement can record measurements of tissue order in the range of10 to 1,000 nanometers. The SAXS measurements may comprise measurementsof scattering of at least about 0.01, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5,0.6, 0.7, 0.8, 0.9, 1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5, 5.5, 6, 6.6, 7,7.5, 8, 8.5, 9, 9.5, 10, or more degrees. The SAXS measurements maycomprise measurements of at most about 10, 9.5, 9, 8.5, 8, 7.5, 7, 6.5,6, 5.5, 5, 4.5, 4, 3.5, 3, 2.5, 2, 1.5, 1, 0.9, 0.8, 0.7, 0.6, 0.5, 0.4,0.3, 0.2, 0.1, 0.05, 0.01, or less degrees. The SAXS measurements maycomprise measurements of a range as defined by any two of the precedingnumbers. For example, the SAXS measurements may comprise measurements ofscattering of 0.1-10 degrees. The SAXS measurements may comprisemeasurements with respect to degrees (e.g., θ), 2θ, d (e.g., distancemeasured in Angstroms), q (e.g., 1/d), or the like, or any combinationthereof.

Wide angle x-ray scattering: In some instances, the one or more tissuediffractometers may be configured to perform wide angle x-ray scattering(WAXS) measurements. The WAXS measurements may comprise measurements ofthe short-range ordering of the tissue. For example, the WAXSmeasurements can record measurements of the tissue order below 10nanometers. The WAXS measurements may provide structural informationabout non-tissue objects in the tissue. For example, a WAXS measurementof an object suspected of being a breast calcification can confirm thatthe object is composed of calcium oxalate and calcium phosphate. Inanother example, a WAXS measurement can generate information regarding amolecular structure within a tissue. The WAXS measurements may comprisemeasurements of at least about 10, 15, 20, 25, 30, 35, 40, 45, or moredegrees. The WAXS measurements may comprise measurements of at mostabout 45, 40, 35, 30, 25, 20, 15, 10, or less degrees. The WAXSmeasurements may comprise measurements of a range as defined by any twoof the preceding numbers. For example, the WAXS measurements cancomprise measurements of scattering of 10-45 degrees. The WAXSmeasurements may comprise measurements with respect to degrees (e.g.,θ), 2θ, d (e.g., distance measured in Angstroms), q (e.g., 1/d), or thelike, or any combination thereof.

Mammography: in some instances, the one or more tissue diffractometersmay be configured to perform mammography. The mammography may beabsorptive, imaging-based mammography. For example, a beam of x-rays canbe projected through a breast and the absorption profile of the breastcan be recorded. The one or more tissue diffractometers may be coupledto, or integrated with, an existing mammography instrument to add tissuediffractometry capabilities to the mammography instrument. Thus, in someinstances, the tissue diffractometers of the present disclosure may beconfigured to perform SAXS, WAXS, mammography, or any combinationthereof. In some instances, the mammography may be performed using anenergy source other than the energy source used to measure diffractionpatterns. For example, the mammography may be performed using a 19 keVx-ray source and the diffraction pattern may be generated using a 75 keVsource. In another example, the mammography can be performed with asource optimized for image quality and contrast, while the diffractionsource can be a molybdenum or silver source. The one or more tissuediffractometers may be configured to perform mammography via scanning.For example, an x-ray beam can be rastered through the breast tissue ofa subject and the absorption of the breast tissue can be recorded. A setof target coordinates for directing an x-ray beam may be determined froma mammogram. For example, a mammogram can be performed and, based on themammogram, areas of ambiguity (e.g., areas suspected of comprising acancer or other diseased tissue) can be identified. In this example, thelocation(s) of the suspect area(s) as determined from the mammogram canbe used to determine target coordinates and direct a tissuediffractometer to measure a diffraction pattern within one of the areasto provide additional information about the state of the area. Thetissue diffractometer may collect diffraction pattern data from the arealocated by the target coordinates while the subject is still in themammography instrument. For example, the target coordinates can begenerated using absorptive imaging mammography, the absorptive imagingmammography x-ray source and detector can be moved, the tissuediffractometer can be moved into position, and the tissue diffractometercan measure diffraction patterns. In this example, the subject canremain in position within the hybrid tissue diffractometer/mammographyinstrument to decrease the complexity of mapping the target coordinatesof the suspect area(s).

Computer database: As noted above, in some instances, the computerdatabase may reside on a central computer server. In some instances, thecentral computer server may reside in the cloud (e.g., may be acloud-based computer server comprising a distributed network of remotecomputer servers). In some instances, the computer database may resideon a local server. In some instances, data may be transferred orexchanged between a local computer database and a remote or centralcomputer database. The computer database may reside on a privacy lawcompliant server (e.g., a HIPAA compliant server).

In some instances, the image data, diffraction pattern data, subjectdata, or any combination thereof transferred to the computer databasemay be depersonalized before transfer. The depersonalization maycomprise removal of personally identifiable information (e.g., name,patient number, social security number, address, etc.). For example,identifying information can be removed from image metadata and/orsubject data before the image metadata and/or subject data aretransferred to the computer database. The depersonalization of the imagedata, diffraction pattern data, subject data, or any combination thereofmay aid in making the computer database compliant with privacy laws. Insome instances, a key for mapping depersonalized image data, diffractionpattern data, subject data, or any combination thereof stored in thecomputer database to an individual subject may be stored in a localinstitutional database and/or in the individual subject's personalfiles. For example, a key can be generated that relates a subject totheir depersonalized data for later reference or reunification. Thelocal institutional database may be a database operated by theinstitution where the subject went to obtain the image data, diffractionpattern data, subject data, or any combination thereof. For example, ahospital can have a database comprising keys to link the identities ofhospital patients to their depersonalized data. In another example, thekey can be kept in the patient's personal medical files.

Data analytics algorithm: As noted above, in some instances the dataanalytics algorithm may comprise a statistical analysis of diffractionpattern data and/or a function thereof. In some instances, the dataanalytics algorithms may comprise a statistical analysis of image data,diffraction pattern data, subject data, a function of any of thepreceding, or any combination thereof. In some instances, thestatistical analysis may comprise determination of a pair-wise distancedistribution function, determination of a Patterson function, acalculation of a Porod invariant, Fourier transformation and calculationof pair-wise distance distribution function, a cluster analysis, adispersion analysis, determination of one or more molecular structuralperiodicities, or any combination thereof. The statistical analysis maycomprise a determination of a structural periodicity of a tissue or atissue feature. The structural analysis may comprise a determination ofa structural periodicity of collagen, one or more lipids, or acombination thereof. For example, a diffraction pattern can provideinformation regarding the structural periodicity, and thus the relativedegree of ordering, of the collagen within the spot size of thediffractometer. In another example, the ordering of lipid layers can bedetermined by diffraction, which can give information about thestiffness of the lipid layers and the chemical composition of the layers(e.g., the amount of cholesterol or other stiffening agents) on a locallevel. In some instances, the structural periodicity of the tissue maybe used to determine a likelihood of a cancer being present within thetissue. For example, FIG. 3A shows an electron microscope image ofnormal collagen tissue, while FIG. 3B shows an electron microscope imageof collagen in an invasive carcinoma tissue. In this example, thecollagen in the normal tissue is more well-structured, which can giverise to stronger diffraction peaks, while the collagen in the carcinomais poorly structured, which can result in weak diffraction peaks.

In some instances, the data analytics algorithm may comprise or furthercomprise the use of one or more machine learning algorithms. The one ormore machine learning algorithms may be configured to operate upon imagedata, diffraction pattern data, subject data, or any combinationthereof. The machine learning algorithm may comprise one or moresupervised learning algorithms, one or more unsupervised learningalgorithms, one or more semi-supervised learning algorithms, one or morereinforcement learning algorithms, one or more deep learning algorithms,or any combination thereof. The machine learning algorithm may be a deeplearning algorithm. The deep learning algorithm may comprise one or moreconvolutional neural networks, one or more recurrent neural networks,and/or one or more recurrent convolutional neural networks.

Statistical analysis algorithms and/or machine learning algorithmsimplemented on a local computer or a remote server may be used toperform data analytics. For example, a machine learning algorithm can beconfigured to pre-process raw image data, diffraction pattern data,and/or subject data to remove noise or other artifacts. A differentmachine learning can be trained to identify features within the imagedata, diffraction pattern data, and/or subject data. Such a machinelearning algorithm can cluster data points for use as an identificationalgorithm. Other machine learning algorithms can be configured toprovide a quantitative diagnostic indicator.

The machine learning algorithms may comprise a supervised,semi-supervised, or unsupervised machine learning algorithm. Asupervised machine learning algorithm, for example, is an algorithm thatis trained using labeled training data sets, e.g., data sets thatcomprise training inputs with known outputs. The training inputs can beprovided to an untrained or partially trained version of the machinelearning algorithm to generate a predicted output. The predicted outputcan be compared to the known output in an iterative process, and ifthere is a difference, the parameters of the machine learning algorithmcan be updated. A semi-supervised machine learning algorithm is trainedusing a large set of unlabeled training data, e.g., unlabeled traininginputs, and a small number of labeled training inputs. An unsupervisedmachine learning algorithm, e.g., a clustering algorithm, may findpreviously unknown patterns in data sets comprising data with nopre-existing labels.

One non-limiting example of a machine learning algorithm that can beused to perform some of the functions described above, e.g., processingof diffraction data, image data, and/or generating quantitativediagnostic indicators, is a neural network. Neural networks employmultiple layers of operations to predict one or more outputs, e.g., alikelihood that a subject has cancer, from one or more inputs, e.g.,image data, diffraction pattern data, subject data, processed dataderived from image data, diffraction pattern data, and/or subject data,or any combination thereof. Neural networks can include one or morehidden layers situated between an input layer and an output layer. Theoutput of each layer can be used as input to another layer, e.g., thenext hidden layer or the output layer. Each layer of a neural networkcan specify one or more transformation operations to be performed on thedata input to the layer. Such transformation operations may be referredto as “neurons”. The output of a particular neuron may be, for example,a weighted sum of the inputs to the neuron, that is optionally adjustedwith a bias and/or multiplied by an activation function, e.g., arectified linear unit (ReLU) or a sigmoid function.

Training a neural network can involve providing inputs to the untrainedneural network to generate predicted outputs, comparing the predictedoutputs to expected outputs, and updating the algorithm's weights andbiases in an iterative manner to account for the difference between thepredicted outputs and the expected outputs. For example, a cost functioncan be used to calculate a difference between the predicted outputs andthe expected outputs. By computing the derivative of the cost functionwith respect to the weights and biases of the network, the weights andbiases can be iteratively adjusted over multiple cycles to minimize thecost function. Training may be complete when the predicted outputssatisfy a convergence condition, such as obtaining a small magnitude ofcalculated cost.

Convolutional neural networks (CNNs) and recurrent neural networks canbe used to classify or make predictions from image data, diffractionpattern data, subject data, or any combination thereof. CNNs are neuralnetworks in which neurons in some layers, called convolutional layers,receive data from only small portions of a data set. These smallportions may be referred to as the neurons' receptive fields. Eachneuron in such a convolutional layer may have the same weights. In thisway, the convolutional layer can detect features, e.g., cancerousgrowths, in any portion of the input image data, diffraction data, or acombination thereof.

RNNs, meanwhile, are neural networks with cyclical connections that canencode dependencies in time-series data, e.g., longitudinal study imagesof one or more subjects. An RNN may include an input layer that isconfigured to receive a sequence of time-series inputs, e.g., imagedata, diffraction pattern data, subject data, or any combination thereofcollected over a period of time. An RNN may also include one or morehidden recurrent layers that maintain a state. At each time step, eachhidden recurrent layer can compute an output and a next state for thelayer. The next state can depend on the previous state and the currentinput. The state can be maintained across time steps and can capturedependencies in the input sequence. Such an RNN can be used to determinetime-series features or evolutions of features within the subject data.

One example of an RNN is a long short-term memory network (LSTM), whichcan be made of LSTM units. An LSTM unit can be made of a cell, an inputgate, an output gate, and a forget gate. The cell can be responsible forkeeping track of the dependencies between the elements in the inputsequence. The input gate can control the extent to which a new valueflows into the cell, the forget gate can control the extent to which avalue remains in the cell, and the output gate can control the extent towhich the value in the cell is used to compute the output activation ofthe LSTM unit. The activation function of the LSTM gate may be, forexample, the logistic function.

Other examples of machine learning algorithms that can be used toprocess image data, diffraction pattern data, subject data, or anycombination thereof are regression algorithms, decision trees, supportvector machines, Bayesian networks, clustering algorithms, reinforcementlearning algorithms, and the like.

The clustering algorithm may be, for example, a hierarchical clusteringalgorithm. A hierarchical clustering algorithm can be a clusteringalgorithm that clusters objects based on their proximity to otherobjects. For example, a hierarchical clustering algorithm can clusterimage data, diffraction pattern data, subject data, or any combinationthereof. The clustering algorithm can alternatively be a centroid-basedclustering algorithm, e.g., a k-means clustering algorithm. A k-meansclustering algorithm can partition n observations into k clusters, whereeach observation belongs to the cluster with the nearest mean. The meancan serve as a prototype for the cluster. In the context of image data,diffraction pattern data, subject data, or any combination thereof, ak-means clustering algorithm can generate distinct groups of data thatare correlated with each other. Thereafter, each group of data can beassociated with, e.g., a particular probability or diagnosis of cancer,based on knowledge about that subsystem, e.g., knowledge about previousdiagnoses and data. The clustering algorithm can alternatively be adistribution-based clustering algorithm, e.g., a Gaussian mixture modelor expectation maximization algorithm. Examples of other clusteringalgorithms are cosine similarity algorithms, topological data analysisalgorithms, and hierarchical density-based clustering of applicationswith noise (HDB-SCAN).

The machine learning algorithm may be trained using a training datasetcomprising image data, diffraction pattern data, subject data, or anycombination thereof. The training dataset may be stored in the computerdatabase for a specific pathology and/or physiological norm group. Thetraining dataset may be obtained using the one or more tissuediffractometers. The training dataset may comprise absorptivemammography images. The training dataset may comprise informationregarding a confirmation of a diagnosis for given set of data. Forexample, data comprising a plurality of images and diffraction patternsof a tissue suspected of being cancerous can also comprise ahistological confirmation of the presence of the cancer in the tissue.In another example, a set of diffraction images can be accompanied bydata regarding the longevity of the subject that the diffraction imageswere taken from. The computer database for the specific pathology and/orphysiological norm group may be a remote computer database (e.g., acloud-based database) or a local database (e.g., a computer system localto a tissue diffractometer). For example, the training dataset forbreast cancer diagnostic indicators can be stored on a computer databasewith other breast cancer diagnostic data. The training dataset may beupdated as new image data, diffraction pattern data, subject data, orany combination thereof is uploaded to the computer database. Theupdating may be an inclusion of the new data, a removal of the old data,or a combination thereof. For example, new image data can be added tothe training dataset as it is taken to improve the quality of thetraining dataset. In another example, poor quality data may be removedfrom the training dataset when higher quality new data is added. Thestatistical analysis algorithm and/or machine learning algorithm (e.g.,the data analytics algorithm) may be updated when the computer databaseor training dataset residing thereon is updated. For example, a machinelearning algorithm can be retrained using the new training dataset toimprove the efficacy of the machine learning algorithm in generating aquantitative diagnostic indicator. The statistical analysis and/ormachine learning algorithm may be continuously, periodically, orrandomly updated and refined as the training dataset is updated. In thisexample, the revised statistical analysis and/or machine learningalgorithm may be more accurate, specific, and/or sensitive in providinga probability or diagnosis than a previous version derived from aprevious training dataset was.

Quantitative diagnostic indicator: In some instances, the quantitativediagnostic indicator for the individual subject may comprise anindicator of a likelihood that the individual subject has a cancer orother disease. The quantitative diagnostic indicator for the individualsubject may comprise an indicator of a likelihood that the individualsubject has breast cancer. For example, a quantitative diagnosticindicator can comprise a banded risk assessment for the individualsubject (e.g., high risk, medium risk, low risk). The quantitativediagnostic indicator may be displayed on a user interface of a device(e.g., a user interface on a computer screen, a user interface on atablet). The quantitative diagnostic indicator may be a report. Thereport may be a printed report. The report may comprise additionalinformation. For example, the report may comprise a likelihood of thesubject having a cancer, as well as the indicators that contributed tothe generation of the report and a suggestion of possible next steps forthe subject to take. The indicator may be a percentage (e.g., apercentage likelihood that the subject has the cancer), a risk band(e.g., high risk, medium risk, low risk), a comparison of factors (e.g.,a list of factor indication a presence and a list of factors indicatingan absence), or the like, or any combination thereof. The indicator ofthe likelihood that the individual subject has cancer may be anindicator of the likelihood that the individual subject has breastcancer.

In some instances, the quantitative diagnostic indicator for theindividual subject may comprise a diagnosis that the individual subjecthas a cancer or other disease. The quantitative diagnostic indicator forthe individual subject may comprise a diagnosis that the individualsubject has breast cancer. The quantitative diagnostic indicator may begenerated at least in part using a statistical analysis algorithm and/ora machine learning algorithm. The quantitative diagnostic indicator maybe generated at least in part using input from a healthcare provider.For example, the healthcare provider can be presented with a list ofindicators and risk bands, and the healthcare provider can make a finaldetermination as to the diagnosis of the subject. In some instances, thequantitative diagnostic indicator may have an accuracy, selectivity,and/or specificity of at least about 10%, 20%, 30%, 40%, 50%, 60%, 70%,80%, 90%, 95%, 98%, 99%, 99.9%, or more. In some instances, thequantitative diagnostic indicator may have an accuracy, selectivity,and/or specificity of at most about 99.9%, 99%, 98%, 95%, 90%, 80%, 70%,60%, 50%, 40%, 30%, 20%, 10%, or less. Any of the lower and upper valuesdescribed in this paragraph may be combined to form a range includedwithin the present disclosure, for example, in some instances thequantitative diagnostic indicator may have an accuracy, selectivity,and/or specificity that ranges from about 80% to about 99%. Those ofskill in the art will recognize that, in some instances, thequantitative diagnostic indicator may have an accuracy, selectivity,and/or specificity that has any value within this range, e.g., about98.6%.

FIG. 1 shows an example of a multi beam x-ray diffraction systemconfigured for acquiring diffraction data for a subject's (e.g., apatient's) breast. The diffraction system comprises a frame 10 thatincludes a breast positioning area 12, an upper optics housing 14 and alower optics housing 16. The upper optics housing 14 and the loweroptics housing 16 include an optics assembly 18. The optics assembly 18includes a radiation source 20, a beam forming apparatus 22 and anadjustable diaphragm 24 positioned in the upper optics housing 14. Theoptics assembly 18 also includes a filter and a two-dimensional detector28 positioned in the lower optics housing 16. The breast positioningarea 12 can include a breast holder 68 such as a single plate on whichthe breast 32 is rested. Because the process of identifying thesubstances which make up an analysis section is not dependent on havinga consistent thickness of the breast 32, the compression of the breast32 which typically occurs in mammography apparatus can be eliminated.The plate should be transparent to the radiation and cause minimalscattering. In another instance the breast positioning area 12 includesa breast holder 68 consisting of an upper plate and a lower plate whichcan be moved toward one another to compress the breast 32 during theanalysis. The plates are constructed from a material which allows theradiation to pass through the plates. Suitable materials for the plateinclude, but is not limited to, polyethylene, non-crystalline glass andsilicon dioxide. In another instance, the breast positioning area doesnot include any structure for supporting the breast 32. During theanalysis the patient need only hold still.

The frame 10 can be rotated about an axis 74 as indicated. The rotationdoes not affect the position of the breast 32 or any breast holder 68within the breast positioning area 12. As a result, the rotation allowsa scan and analysis from particular projections. The axis 74 of rotationis as close to the center of the breast 32 as is possible to preservethe distance between the detector 28 and the breast 32 at eachprojection.

In operation, the beam forming apparatus 22 forms the radiation into aweakly diverging incident beam 30. The incident beam 30 is sufficientlylong to be incident on one entire dimension of a breast 32 positioned inthe breast positioning area 12. In FIG. 1 , the breast 32 is positionedso the incident beam 30 is incident on the entire width of the breast32, i.e. the length of the incident beam 30 extends into and out of theplane of the page. The beam forming apparatus 22 is preferablypositioned around 10 cm from the upper surface 34 of the breast 32. Asuitable beam forming apparatus 22 includes, but is not limited to, aKratki collimator.

The incident beam 30 passes from the beam forming apparatus 22 throughthe breast 32 to the detector 28. The detector 28 receives radiation ina transmitted beam zone 38 and a scattering zone 40. The transmittedbeam zone 38 receives the transmitted beam 42 and the scattering zone 40receives radiation scattered outside the transmitted beam 42 by thebreast 32.

The beam forming apparatus 22 can include a plurality of transparentchannels 90 and opaque channels 92 as illustrated in FIG. 1 . Theradiation from the radiation source 20 passes through the transparentchannels 90 to form a plurality of weakly divergent incident beams 30.Each incident beam 30 has a length sufficient to cover the entire widthof the breast 32. The beam forming apparatus 22 allows a plurality ofanalysis sections to be analyzed during a single exposure of the breast32. Accordingly, the subject's exposure time can be reduced. Thetransparent channels 90 of the beam forming apparatus 22 are orientedalong directions converging at a point coinciding with the focal pointof the radiation source 20. Suitable beam forming apparatuses 22include, but are not limited to, a slit raster. Further, suitable shapesand arrangements for the transparent channels 90 include, but are notlimited to, slits and round apertures located at vertices of hexagonalor square lattices. The transparent channels 90 should converge at thefocal spot of the source to increase energy yield of the device. Thebeam forming apparatus 22 can form incident beams 30 which are spacedalong an entire dimension of the breast 32, however, the overlap ofscattered radiation from adjacent transmitted beams 42 should beminimized. When the beam forming apparatus 22 is a slit raster, suitablewidths for the transparent channels 90 include, but are not limited to20-120 μm, 40-80 μm, and 55-65 μm. When the beam forming apparatus 22 isa slit raster, the width of the opaque sections depends on the desirednumber of incident beams which are incident on the breast. A suitablewidth of the opaque section includes, but is not limited to, 0.5centimeter. When the beam forming apparatus 22 is a slit raster,suitable depths for the transparent channels are on the order of 100 mmdepending on the desired divergence of the incident beam. Suitable beamdivergences include, but are not limited, to 1-10 arc seconds. Theplurality of incident beams 30 can reduce the scan time. For instance,when the incident beams 30 are evenly spaced across the length 72 of thebreast 32, the upper optics housing 14 can move a distance roughly equalto the displacement between the incident beams 30 to scan the entirebreast 32. This reduced scan time helps to increase the subject'scomfort.

FIG. 2A shows an example of a disordered molecule and an orderedmolecule. The disordered molecule 201 may exhibit less efficient packingthan the ordered molecule 202. As such, a diffraction pattern taken of aplurality of disordered molecules may have less prominent peaks due tothe lack of ordered packing. For example, an x-ray diffraction image ofsolid sodium oleate can be broader than the corresponding x-raydiffraction image of sodium stearate due to the stearate having lessdisorder. The relationship between ordering and x-ray diffractionpattern intensity can be extended to larger biological systems (e.g.,proteins, cell walls, fibers, muscles, etc.). For example, awell-ordered array of muscle fibers can have a stronger x-raydiffraction pattern than a disordered array. If a biological systemshows a different ordering behavior when it is healthy versus anomalous,x-ray diffraction can provide information about the health of thetissue. For example, a mammograph can show small white deposits in abreast. In this example, an unhealthy breast tissue with solid calciumdeposits can be discerned from dense healthy breast tissue using WAXS todetermine the molecular identity of the white deposits. In anotherexample, collagen tends to be well ordered in healthy tissues butdisordered in unhealthy tissues. In this example, a SAXS measurement ofthe tissue can determine the disorder state of the collagen and thusdetermine if the tissue is abnormal. Examples of deposits discernable byWAXS may include urea deposits (e.g., in gout), calcium deposits (e.g.,calcium deposits in breast tissue), other organic crystals (e.g.,proteins), other inorganic crystals (e.g., calcium fluoride),organic-inorganic crystalline hybrids (e.g., hemoglobin buildup), or thelike, or any combination thereof. Examples of conditions discernable bySAXS may include cancers (e.g., carcinoma), plaque buildups, musculardiseases (e.g., atrophy), subcutaneous warts, or the like.

FIG. 2B shows an example of the scales of various biological objects.The different scales may highlight the advantage of measuring SAXS,WAXS, absorptive mammography images, or any combination thereof at asame time. For example, an absorptive mammography image can have aresolution of approximately 0.1 millimeters per pixel, which can make itimpossible to view features on the order of 100 micrometers or below. Inthis example, SAXS can provide details about the presence of ordering onthe order of 10-10,000 nanometers and WAXS can provide details about thepresence of ordering on the order of 0.1-10 nanometers. As shown in FIG.2B, much of the fibrillar collagen in breast tissue may be of a scale ofless than 100 micrometers, so information about the ordering of thecollagen can be undetected by absorptive mammography but can be detectedusing x-ray scattering methods. Because tissue abnormalities can resultin a change in the ordering of the tissue (e.g., cancer can disruptcollagen ordering), SAXS and/or WAXS can be a valuable addition toabsorptive mammography.

FIGS. 3A-3B show examples of electron micrographs of collagen in normaltissue and in invasive carcinoma tissue. The healthy collagen 301 inFIG. 3A may present as a well-ordered tissue. The ordering of thefibrils, along with the homogenous nature of all of the tissue, can giverise to a strong diffraction pattern with peaks corresponding to theaxial and meridional lengths of the collagen. The presence of thesepeaks can be an indicator of a healthy tissue. For example, a machinelearning algorithm can be trained in part using healthy collagen tissuesto associate the presence of diffraction peaks corresponding to theaxial and meridional dimensions of the collagen with a healthy tissue.The invasive carcinoma tissue 302 of FIG. 3B may lack the long rangerordering of the healthy tissue 301. As a result, a diffraction patterntaken of carcinoma tissue 302 may have weak or non-existent collagenpeaks. This lack of peaks may be an indication of an invasive carcinoma.For example, a machine learning algorithm can use the lack of collagenpeaks where there are expected to be collagen peaks as an indication ofa presence of an invasive carcinoma.

FIG. 4 shows x-ray diffraction data generation. Incoming x-ray beam 401can be directed towards fibril bundle 402. The periodicity of fibrilbundle 402 can give rise to x-ray diffraction, which can be recorded asx-ray diffraction pattern 403. The intensity along the meridional axisof diffraction pattern 403 can be taken from the center of the patternto the edge and can be plotted as meridional scattering profile 404. Themeridional scattering profile can comprise peaks 405. The peaks can bethe result of constructive interference in the x-ray diffraction givingrise to bands of higher intensity. In this example, the periodicity 406of the fibrils can give rise to the diffraction peaks, with each peakrepresenting a different nearest neighbor distance. The intensity alongthe equatorial direction of diffraction pattern 403 can be taken fromthe center of the diffraction pattern to the edge and plotted asequatorial scattering profile 407. The equatorial scattering profile maycomprise peaks 408 that can be related to the packing of the fibrils aswell as the size of the fibrils. In this example, since the fibrils arepacked at a larger distance than the diameter 409 of the fibrils, thepeak from the packing may be at a lower q value (e.g., larger distance)than the peaks from the fibril size.

The image data, diffraction pattern data, or a combination thereof mayundergo statistical analysis. The statistical analysis may comprisedetermination of a pair-wise distance distribution function,determination of a Patterson function, a calculation of a Porodinvariant, a cluster analysis, a dispersion analysis, determination ofone or more molecular structural periodicities, or any combinationthereof. The pair-wise distance distribution function may be derivedfrom the x-ray diffraction data. The pair-wise distance distributionfunction may be a distribution of neighbors to a scatterer (e.g., anatom, a fibril, etc.). For example, a pair-wise distribution function ofan atomic lattice can show the distribution of nearest neighbors to theatom as a function of distance. In another example, a pair wisedistribution function of collagen fibrils can show the probability offinding another collagen fibril as a function of distance from the firstfibril. The pair-wise distance distribution function may provideinformation regarding the local structure around the scatterer. ThePatterson function may provide information related to the phasecomponent of an x-ray diffraction based on the intensity of thediffraction pattern. The Patterson function may provide informationregarding the electron density of the local environment of the specimen.The Porod invariant may be a model independent invariant that may beused to determine a volume fraction of the sample. The Porod invariantmay depend on the volume of the scatterer, but not the form. Forexample, a sphere of volume 1 cubic micron and a cube of volume 1 cubicmicron can have a same value of the Porod invariant. The clusteranalysis may be a statistical analysis of the diffraction data. Thecluster analysis may determine a presence of one or more clusters in thedata. For example, diffraction data can be analyzed to determineclusters of data based on one or more components of the data. Thedispersion analysis may determine a dispersion of different types offeatures within the sample. For example, a dispersion analysis of thesize of scattering fibrils can generate a distribution of the differentsizes of fibrils in the sample. The dispersion analysis may determinethe dispersion shape, dispersion width, dispersion modality (e.g.,bimodal), or the like, or any combination thereof. The determination ofone or more molecular structural periodicities may be a determination ofthe crystal structure of a molecular array. For example, the structureof a urea crystal within a sample can be determined. In another example,the structural periodicity of a light spot in a mammogram can bedetermined to identify the composition of the light spot. Thedetermination of one or more molecular structural periodicities mayprovide an indicator of the identity of the molecule.

FIG. 8 shows an example of a combined mammography and x-ray diffractioninstrument. The mammography portion of the instrument may be amammography instrument made by a health instrumentation manufacturer(e.g., Siemens, GE, Philips). The mammography portion may use an x-raysource to project a beam of x-rays through a breast of a subject andmeasure the transmission of the x-rays. The transmission of the x-raysmay be affected by a number of different factors, such as tissuedensity, tissue composition, presence of non-tissue species (e.g.,calcium deposits), or the like. The x-ray source may be a radioisotope,an x-ray tube (e.g., a hot or cold cathode tube, a rotating anode tube,or the like), an x-ray laser, a plasma source, a synchrotron, acyclotron, or the like. The x-ray source may generate collimated x-rays.The transmission of the x-rays may be detected by an x-ray detector asdescribed elsewhere herein. The x-ray detector may be a film detector ora digital detector. The mammography portion of the instrument may becontrolled by software. The software may comprise a user interfaceconfigured to display one or more mammography images to the subjectand/or the healthcare provider. A healthcare provider may administer anx-ray contrast agent before taking a mammogram. For example, thehealthcare provider can provide an iodine injection prior to taking themammogram to aid in the visualization of blood vessels. The x-raydiffraction modality may provide strong signals in an absence of acontrast agent. For example, since the interaction between the x-raybeam and the tissue that produces the diffraction pattern can beindependent of absorptive contrast, the diffraction pattern can be takenequally well with and without administering a contrast agent.

The x-ray diffraction modality may comprise an x-ray source 801 and anx-ray detector 802. The x-ray diffraction modality may be added to anexisting mammography instrument (e.g., the x-ray diffraction modality isattached to a mammography instrument). The x-ray source may be an x-raysource as described elsewhere herein. The x-ray source may be aplurality of x-ray sources. For example, the x-ray source can comprise ahigh energy x-ray source and a low-energy x-ray source. In this example,the x-ray source can be selected based on the properties of the x-raysthat can generate the optimal diffraction data. The x-ray source maygenerate a plurality of x-ray beams. The x-ray detector may be an x-raydetector as described elsewhere herein. The x-ray detector may be aplurality of x-ray detectors. One or more optical elements may be placedin the beam path 803 between the x-ray source and the x-ray detector.The one or more optical elements may be one or more phase plates, beamshaping elements (e.g., slits), diffraction gratings, single crystals,lenses, gratings, phosphor layers, power meters, or the like, or anycombination thereof. The x-ray source and/or the x-ray detector may bemovable. For example, the x-ray detector can be movable between twodifferent distances from the x-ray source to change from a SAXS mode toa WAXS mode. The x-ray detector, x-ray source, one or more opticalelements, or any combination thereof, may be configured to be controlledby one or more computer processors. The one or more computer processorsmay control the x-ray source by programming a voltage and/or a currentfor the source, controlling a shutter state, selecting an x-ray sourcefrom a plurality of x-ray sources, or the like, or any combinationthereof. The one or more computer processors may control the one or moreoptical elements by adjusting a position of the one or more opticalelements (e.g., moving a lens, changing a width of an adjustable slit),changing a phase of a phase plate, adjusting a transmission of avariable filter, or the like, or any combination thereof. The one ormore computer processors may control the x-ray detector by programming abinning of the detector, setting an exposure time of the detector,selecting an active detector, adjusting parameters of the detector(e.g., gain, contrast, gamma), or the like, or any combination thereof.One or more visible light sources (e.g., lasers) may be used to showwhere the one or more x-ray beams will interact with the tissue.

FIG. 9 shows an example top view of a breast being probed by an x-raydiffraction system. Points 1001 can be locations of incident x-ray beamson breast 1002. The incident x-ray beam may be a plurality of incidentx-ray beams. The incident x-ray beams may be configured to generatediffraction pattern data around each point as shown by the circularsensitivity areas around each point. For example, the diffractionpattern data can be sensitive within 2 inches around the x-ray beam. Thesensitivity areas of some of the x-ray beams can overlap with apotential cancer site 1003. Since the sensitivity area overlaps thepotential cancer site, diffraction data indicative of the presence orabsence of the cancer can be generated. By projecting a plurality ofbeams through a tissue (e.g., a breast), the entire tissue may be testedfor a presence of an abnormality. In the example of FIG. 9 , the fivebeams can generate signals indicative of the presence of a cancer at thepotential cancer site 1003. In this example, the presence of the signalcan lead to a determination of an approximate position of the cancer,which can allow for more detailed investigation. The diffraction patterndata may be processed. The processing may comprise intensitynormalization, calibration, glitch removal, detector dead timecorrection, scaling, baselining, or the like, or any combinationthereof. The processing may comprise applying one or more machinelearning algorithms to the diffraction pattern data. For example, thepoints that are far from the potential cancer site may be used ascontrol regions, while the points nearer to the potential cancer sitecan be analyzed for the presence or absence of the cancer. The x-raybeams may interact with the tissue at a same time as an absorptivemammography image is taken.

FIG. 10 shows a schematic of a plurality of tissue diffractometersoperatively coupled to a computer database over a network. The pluralityof tissue diffractometers operatively coupled to the computer databaseover the network may be a global diagnostics system 1000. The globaldiagnostics system may comprise a computer database 1010. The computerdatabase may be configured to store data (e.g., image data, diffractionpattern data, subject data, or any combination thereof). The centralcomputer database may be encrypted. The computer database may beconfigured for compliance with health data privacy laws and regulations(e.g., HIPAA). The computer database may be a distributed computerdatabase (e.g., a cloud-based database housed at a plurality oflocations). The computer database may be configured to accept data fromone or more tissue diffractometers 1030, 1040, 1050, and/or 1060 via oneor more computer processor(s) 1020. The one or more computer processorsmay be configured to pre-process, process, and/or post-process the dataas described elsewhere herein. The one or more computer processors maybe coupled to the one or more tissue diffractometers via a network(e.g., a local network, the internet, a virtual private network). Theone or more tissue diffractometers may be at least about 1, 5, 10, 25,50, 75, 100, 250, 500, 750, 1,000, 2,500, 5,000, 10,000, 50,000, 100,000or more tissue diffractometers. The one or more tissue diffractometersmay be at most about 100,000, 50,000, 10,000, 5,000, 2,500, 1,000, 750,500, 250, 100, 75, 50, 25, 10, 5, or less tissue diffractometers. Theone or more tissue diffractometers may be one or more of a same type oftissue diffractometer (e.g., a same model), or one or more of adifferent type of tissue diffractometers (e.g., one or more differentmodels of tissue diffractometers). The computer processors 1020 may beconfigured to periodically refine and update a statistical and/ormachine learning based data analytics algorithm using data stored in thecomputer database 1010. For example, the data analytics algorithm may beupdated every month, every week, every day, or every hour. In someinstances, the computer processors 1020 and computer database 1010 maybe configured to continually refine a statistical and/or machinelearning based data analytics algorithm. For example, each time new datais received from a tissue diffractometer, the computer processors 1020can access that new data from the computer database 1010 to update thedata analytics algorithm. The data analytics algorithm may be a dataanalytics algorithm and/or machine learning algorithm as describedelsewhere herein.

FIG. 11 shows an example schematic for a data collection and processingworkflow 1100. In an operation 1110, the process 1100 may compriseacquiring diffraction data, image data, subject data, or any combinationthereof for an individual subject. The acquiring may be acquiring usingan absorptive mammography instrument, a diffraction-based instrument, ora combination thereof. For example, a combined mammography anddiffraction instrument can acquire both absorptive mammography imagesand tissue diffraction patterns. The acquiring may be in a singlesession. For example, subject data comprising medical history andancestral medical information can be acquired through an interview withthe subject before a mammogram is taken. The acquiring may be over aplurality of sessions. For example, a time series of mammography imagesand diffraction patterns can be taken over a period of time to track achange in a cancer state of a subject. The image data, diffraction data,subject data, or any combination thereof may be data as describedelsewhere herein. The acquiring may be performed by one or more tissuediffractometers as described elsewhere herein.

In another operation 1120, the process 1100 may comprise transferringencrypted data to a computer database. The encrypted data may compriseimage data, diffraction pattern data, subject data, or any combinationthereof for one or more individual subjects. For example, the encrypteddata can comprise all of the data taken from a radiology clinic in aday. In another example, the encrypted data can be data from anindividual subject served by a radiology clinic. The encrypted data maybe encrypted using an asymmetric key encryption, a symmetric keyencryption, or the like. The encrypted data may be encrypted by acomputing device local to where the data was generated (e.g., a computeroperatively coupled to a tissue diffractometer). The encrypted data maybe stored locally before being transferred to the computer database. Theencrypted data may be streamed (e.g., transferred in real-time orsubstantially real-time) to the computer database. The computer databasemay be a local computer database (e.g., a local computing cluster housedin the same facility as where the data was acquired) or a remotecomputer database (e.g., a cloud computing database). The encrypted datamay be uncompressed data or compressed data.

In another operation 1130, the process 1100 may comprise processing datafor the individual subject using a data analytics algorithm. Theprocessing may be performed on one or more computer processors asdescribed elsewhere herein. The processing may be encoded on anon-transitory computer readable medium. The data analytics algorithmmay be a statistical analysis algorithm and/or a machine learningalgorithm. The data analytics algorithm may be a convolutional neuralnetwork as described elsewhere herein. The data analytics algorithm mayperform pre-processing, processing, and/or post-processing ofdiffraction data, image data, subject data, or any combination thereof.The pre-processing may comprise denoising (e.g., removing nose from thedata), normalizing (e.g., standardizing data properties such as size,black level, maximum intensity, etc.), segmentation (e.g., dividing thedata into sections comprising different features), masking (e.g.,applying one or more masks to the data), enhancing edges and/orfeatures, or the like, or any combination thereof. The processing maycomprise determining a presence or absence of a feature in the data(e.g., determining a presence of a cancerous spot in a mammography imageby in part using a breast tissue diffraction pattern), determining aseverity of a feature in the data (e.g., determining the progression ofa cancer), clustering data (e.g., clustering images based on thepresence or absence of a feature), predicting a presence or absence of afeature in new data (e.g., using previously acquired images to generatea prediction of a presence of a feature in a new set of data), or thelike, or any combination thereof. The post-processing may compriseformatting (e.g., formatting data for presentation to a subject or ahealthcare worker), denoising, normalizing, masking, enhancingproperties (e.g., contrast, edges), or the like, or any combinationthereof.

In another operation 1140, the process 1100 may comprise generating adiagnostic indicator for the individual subject. The diagnosticindicator may be a quantitative diagnostic indicator. The computer aideddiagnostic indicator may be a computer readable report, a human readablereport, or both. For example, the computer aided diagnostic indicatorcan be a report displayed on a user interface of a device. Thediagnostic indicator may comprise information about a likelihood of apresence of a feature in the data (e.g., a presence of breast cancer inmammography and diffraction data), a severity of a presence of a feature(e.g., a prognosis based on the severity of the feature), one or moresuggested treatments (e.g., a suggestion of a mastectomy for a severebreast cancer), additional information (e.g., locations of resources tohelp the subject understand the diagnostic indicator), subject data(e.g., the name of the subject the indicator is for), or the like, orany combination thereof. The diagnostic indicator may be generated on asame computer system as the data analytics algorithm was run on. Thediagnostic indicator may be held until the subject or the healthcareprovider provides an input. The input may be a payment (e.g., a paymentfrom the subject, a payment from the subject's insurance), an agreementfor the subject's data to be used for training and/or validating futuredata analytics algorithms, or the like, or any combination thereof. Forexample, the subject can be informed that the diagnostic indicator isready, and that the subject can sign a waiver allowing use of thesubject's data.

In another operation 1150, the process 1100 may comprise updating thecomputer database with the image data, diffraction data, subject data,or any combination thereof generated for a plurality of subjects using aplurality of tissue diffractometers. The updating may make additionaldata available to train a new data analytics algorithm or update anexisting data analytics algorithm. The computer database may be updatedwith indicators of a confirmation of an indication made in a diagnosticindicator. For example, the database can be updated with informationregarding the surgical confirmation of cancer in a patient for whom thediagnostic indicator indicated a likelihood of cancer. This updating mayprovide a confirmation of positive or negative results that can improvethe accuracy of future diagnostic indicators. The data may beagglomerated for the plurality of subjects to generate a generalclassifier. For example, a database of breast images and diffractionpatterns can be used to generate a classifier for breast tissue. Inanother example, a database of brain images and diffraction patterns canbe used to generate a classifier for brain tissues.

In another operation 1160, the process 1100 may comprise refining thedata analytics algorithm. The refining may comprise generating a newdata analytics algorithm. The refining may comprise an updating ofweights or other components within the data analytics algorithm. Forexample, the neural weights of a neural network can be updated based onthe additional data from the plurality of subjects. The refining of thedata analytics algorithm may improve the sensitivity, specificity,accuracy, or any combination thereof of the data analytics algorithm.The refined data analytics algorithm may be used to process the data foranother subject (e.g., used as the data analytics algorithm of operation1130).

The methods and systems of the present disclosure may be applied fordiagnostic purposes. For example, the presence of a cancer in a breastof a subject can be diagnosed using a combination of mammography anddiffraction pattern data. The diagnostic purposes may include cancerdiagnosis, muscular condition diagnoses (e.g., muscular degeneration),optometric diagnoses (e.g., corneal damage, other eye diseases), bonecondition diagnoses (e.g., osteoporosis), other tissue diagnoses (e.g.,brain degeneration), or the like, or any combination thereof. Thegeneration of diffraction pattern data may be combined with mammographyinstruments, chest x-ray instruments, skull x-ray instruments, limbx-ray instruments, C-arm x-ray instruments, or the like. For example, aC-arm x-ray instrument can comprise two optical paths, one forabsorptive imaging and another for diffraction pattern data generation.

The present disclosure also provides computer systems that areprogrammed to implement methods of the disclosure. FIG. 12 shows acomputer system 1201 that is programmed or otherwise configured toimplement methods described elsewhere herein (e.g., obtaining data fromone or more tissue diffractometers, processing the data, etc.). Thecomputer system 1201 can regulate various aspects of the presentdisclosure, such as, for example, the processing of image data,diffraction pattern data, subject data, or any combination thereof. Thecomputer system 1201 can be an electronic device of a user or a computersystem that is remotely located with respect to the electronic device.The electronic device can be a mobile electronic device. The computersystem 1201 may be a post-classical computer system (e.g., a quantumcomputing system).

The computer system 1201 includes a central processing unit (CPU, also“processor” and “computer processor” herein) 1205, which can be a singlecore or multi core processor, or a plurality of processors for parallelprocessing. The computer system 1201 also includes memory or memorylocation 1210 (e.g., random-access memory, read-only memory, flashmemory), electronic storage unit 1215 (e.g., hard disk), communicationinterface 1220 (e.g., network adapter) for communicating with one ormore other systems, and peripheral devices 1225, such as cache, othermemory, data storage and/or electronic display adapters. The memory1210, storage unit 1215, interface 1220 and peripheral devices 1225 arein communication with the CPU 1205 through a communication bus (solidlines), such as a motherboard. The storage unit 1215 can be a datastorage unit (or data repository) for storing data. The computer system1201 can be operatively coupled to a computer network (“network”) 1230with the aid of the communication interface 1220. The network 1230 canbe the Internet, an internet and/or extranet, or an intranet and/orextranet that is in communication with the Internet. The network 1230 insome cases is a telecommunication and/or data network. The network 1230can include one or more computer servers, which can enable distributedcomputing, such as cloud computing. The network 1230, in some cases withthe aid of the computer system 1201, can implement a peer-to-peernetwork, which may enable devices coupled to the computer system 1201 tobehave as a client or a server.

The CPU 1205 can execute a sequence of machine-readable instructions,which can be embodied in a program or software. The instructions may bestored in a memory location, such as the memory 1210. The instructionscan be directed to the CPU 1205, which can subsequently program orotherwise configure the CPU 1205 to implement methods of the presentdisclosure. Examples of operations performed by the CPU 1205 can includefetch, decode, execute, and writeback.

The CPU 1205 can be part of a circuit, such as an integrated circuit.One or more other components of the system 1201 can be included in thecircuit. In some cases, the circuit is an application specificintegrated circuit (ASIC).

The storage unit 1215 can store files, such as drivers, libraries andsaved programs. The storage unit 1215 can store user data, e.g., userpreferences and user programs. The computer system 1201 in some casescan include one or more additional data storage units that are externalto the computer system 1201, such as located on a remote server that isin communication with the computer system 1201 through an intranet orthe Internet.

The computer system 1201 can communicate with one or more remotecomputer systems through the network 1230. For instance, the computersystem 1201 can communicate with a remote computer system of a user(e.g., a cloud server). Examples of remote computer systems includepersonal computers (e.g., portable PC), slate or tablet PC's (e.g.,Apple® iPad, Samsung® Galaxy Tab), telephones, Smart phones (e.g.,Apple® iPhone, Android-enabled device, Blackberry®), or personal digitalassistants. The user can access the computer system 1201 via the network1230.

Methods as described herein can be implemented by way of machine (e.g.,computer processor) executable code stored on an electronic storagelocation of the computer system 1201, such as, for example, on thememory 1210 or electronic storage unit 1215. The machine executable ormachine-readable code can be provided in the form of software. Duringuse, the code can be executed by the processor 1205. In some cases, thecode can be retrieved from the storage unit 1215 and stored on thememory 1210 for ready access by the processor 1205. In some situations,the electronic storage unit 1215 can be precluded, andmachine-executable instructions are stored on memory 1210.

The code can be pre-compiled and configured for use with a machinehaving a processor adapted to execute the code, or can be compiledduring runtime. The code can be supplied in a programming language thatcan be selected to enable the code to execute in a pre-compiled oras-compiled fashion.

Aspects of the systems and methods provided herein, such as the computersystem 1201, can be embodied in programming. Various aspects of thetechnology may be thought of as “products” or “articles of manufacture”typically in the form of machine (or processor) executable code and/orassociated data that is carried on or embodied in a type of machinereadable medium. Machine-executable code can be stored on an electronicstorage unit, such as memory (e.g., read-only memory, random-accessmemory, flash memory) or a hard disk. “Storage” type media can includeany or all of the tangible memory of the computers, processors or thelike, or associated modules thereof, such as various semiconductormemories, tape drives, disk drives and the like, which may providenon-transitory storage at any time for the software programming. All orportions of the software may at times be communicated through theInternet or various other telecommunication networks. Suchcommunications, for example, may enable loading of the software from onecomputer or processor into another, for example, from a managementserver or host computer into the computer platform of an applicationserver. Thus, another type of media that may bear the software elementsincludes optical, electrical and electromagnetic waves, such as usedacross physical interfaces between local devices, through wired andoptical landline networks and over various air-links. The physicalelements that carry such waves, such as wired or wireless links, opticallinks or the like, also may be considered as media bearing the software.As used herein, unless restricted to non-transitory, tangible “storage”media, terms such as computer or machine “readable medium” refer to anymedium that participates in providing instructions to a processor forexecution.

Hence, a readable medium, such as computer-executable code, may takemany forms, including but not limited to, a tangible storage medium, acarrier wave medium or physical transmission medium. Non-volatilestorage media include, for example, optical or magnetic disks, such asany of the storage devices in any computer(s) or the like, such as maybe used to implement the databases, etc. shown in the drawings. Volatilestorage media include dynamic memory, such as main memory of such acomputer platform. Tangible transmission media include coaxial cables;copper wire and fiber optics, including the wires that comprise a buswithin a computer system. Carrier-wave transmission media may take theform of electric or electromagnetic signals, or acoustic or light wavessuch as those generated during radio frequency (RF) and infrared (IR)data communications. Common forms of computer-readable media thereforeinclude for example: a floppy disk, a flexible disk, hard disk, magnetictape, any other magnetic medium, a CD-ROM, DVD or DVD-ROM, any otheroptical medium, punch cards paper tape, any other physical storagemedium with patterns of holes, a RAM, a ROM, a PROM and EPROM, aFLASH-EPROM, any other memory chip or cartridge, a carrier wavetransporting data or instructions, cables or links transporting such acarrier wave, or any other medium from which a computer may readprogramming code and/or data. Many of these forms of computer readablemedia may be involved in carrying one or more sequences of one or moreinstructions to a processor for execution.

The computer system 1201 can include or be in communication with anelectronic display 1235 that comprises a user interface (UI) 1240 forproviding, for example, an interface for a healthcare or an individualsubject to upload image data, diffraction pattern data, subject data, orany combination thereof to a computer database. Examples of UI'sinclude, without limitation, a graphical user interface (GUI) andweb-based user interface.

Methods and systems of the present disclosure can be implemented by wayof one or more algorithms. An algorithm can be implemented by way ofsoftware upon execution by the central processing unit 1205. Thealgorithm can, for example, be a machine learning algorithm as describedelsewhere herein.

EXAMPLES

The following examples are illustrative of certain systems and methodsdescribed herein and are not intended to be limiting.

Example 1 X-Ray Diffraction Measurements of Breast Tissue

FIGS. 5A-5B show examples of small angle x-ray diffraction data. Thenormal tissue diffraction pattern of FIG. 5A exhibits clear peaks in thediffraction pattern, such as those called out as y-axis peak 501 andx-axis peak 502. The asymmetry of the size and distance of peaks 501 and502 can be indicative of different size domains in each direction.Because the 2D diffraction pattern is in q space (e.g., with a dimensionof 1/nm), the peak 501 can be indicative of shorter-range ordering ascompared to peak 502 at lower q and thus larger range. The presence ofdiffraction spots 503 can be indicative of a presence of highly orderedand homogenous patterns (e.g., lines, lattices of points) within thetissue. Based on this diffraction pattern, the presence of asymmetriclong- and short-range ordering can be an indicator of healthy breasttissue, such as a presence of uninterrupted collagen in the tissue orthe presence of undisturbed fibrils. In contrast, the diffractionpattern of FIG. 5B, which was taken of breast tissue with a knowndisease, shows weak, symmetric diffraction peaks 504. As compared to thepeaks in FIG. 5A, peaks 504 are weak and do not demonstrate asymmetricordering. This is because the disease in the breast tissue results in adisruption of the tissue and a destruction of long-range order. Forexample, the tissues of FIGS. 3A-3B show the difference between healthy,ordered tissues and unhealthy, disordered tissues that can give rise todiffraction patterns as seen in FIGS. 5A-5B.

FIGS. 6A-6B show examples of small angle x-ray diffraction data from abreast tissue with a known disease. The diffraction pattern of FIG. 6Acan be radially integrated (e.g., a slice of the image can be taken androtated about the center as shown on the figure) to generate a 2Ddiffraction pattern as seen in FIG. 6B. Each of the high intensityregions of FIG. 6A can give rise to a peak in the diffraction pattern ofFIG. 6B. While radially integrating can remove information about adistribution of the signals between axial and equatorial components, itcan also improve signal to noise and provide a convenient way to displaydiffraction data. Additionally, knowing the approximate q value of thepeak from the diffraction image can enable tracking of the peak in thediffraction pattern, as one can relate the q position from the image toa peak in the integrated pattern. The diffraction pattern of FIG. 6B isread where the center of the diffraction image corresponds to the farleft of the pattern, and larger q corresponds to the edges of thediffraction image. Since q is in units of inverse space, the higher qpeaks correspond to smaller spacings. For example, the fifth axial peakof FIG. 6B corresponds to an ordering with a feature size ofapproximately 0.5 inverse nanometers. The intensities of the peaks, aswell as the position, width (e.g., full width at half maximum), shape(e.g., gaussian shape, Lorentzian shape), or any combination thereof canbe indicators of the order state of the tissue, which can be related toa presence or absence of a disease. Diffraction images such as that ofFIG. 6A, as well as diffraction patterns such as that of FIG. 6B can beused as input data for a machine learning algorithm configured to detecta presence or absence of a cancer.

FIG. 7 shows an example of data extracted from x-ray diffractionpatterns that has been plotted to illustrate the clustering fordifferent tissue types. In this example, the intensity of two peaks, d1and d2, in a plurality of datasets were plotted against each other toform the scatter plot. A convolutional neural network can then processthe data to cluster the points based not only on the relativeintensities of the two peaks, but also based on other data from thediffraction patterns and related mammography data. The neural networkcan then assign indicators to each of the clusters to aid in adiagnosis. In this example, the clusters were labeled as non-cancerous(N), malignant (M), suspicious (B), and indicative of calcium deposits(C). In an example, a new diffraction pattern is processed and shows ad1 peak intensity of 0.91 and a d2 peak intensity of 1.05. In thisexample, the new diffraction pattern can be classified as being of amalignant breast sample, as the new data fits best with the malignantcluster. The neural network can then be updated with the new data toimprove the effectiveness of the neural network for future data.

Another application of the disclosed methods and systems is monitoringof therapeutic efficacy for cancer treatments or disease therapeutics(e.g., drugs). Subjects (e.g., patients) may undergo one or more followup examinations at various time points following the initiation of,during treatment by, or after completion of a therapeutic treatment, andthe clustering of data derived from the analysis of image data,diffraction data, subject data, or any combination thereof, is analyzedand re-evaluated for changes in sample data characteristics andclustering. As a result, the data analytics algorithm may, for example,plot patient sample data points in an n-dimensional space defined by twoor more treatment parameters that describe the clustering of the sampledata, and the distance or changes in distance between different clustersmay be calculated as a function of time. In some instances, for example,the proximity of a new data point to the previous data point(s), or thetrajectory of certain data clusters (or the gradient of the trajectory)may be used as an indicator for the therapy's effectiveness and can beinterpreted by physician in terms of therapeutic efficiency. In someinstances, the output of the data analytics algorithm (e.g., aquantitative diagnostic indicator) generated for multiple examinationsmay be used directly to monitor the efficacy of a therapeutic treatment.Comparing the results of follow up assessments for multiple patients'samples may provide indications of the efficiency of certain drugs andtreatments in specific groups of patients.

The invention may also be described by the following numberedparagraphs:

-   -   1. A method of in vivo human-tissue analysis and communication        that produces a quantitative diagnostic indicator for        human-tissue, comprising: analyzing human tissue and producing        an quantitative-diagnostic indicator; and sending and receiving        information relevant to the quantitative-diagnostic indicator.    -   2. The method of paragraph 1, further including the steps of        operatively coupling at least one tissue diffractometer to a        computer database over a network, and acquiring human-tissue        data chosen from the group consisting of in situ image data, in        situ diffraction pattern data, and subject data, and transfer of        the human-tissue data to the computer database over the network.    -   3. The method of paragraph 2, further including the steps of        operatively coupling at least one computer processor to the at        least one tissue diffractometer, and configuring the at least        one computer processor to receive the human-tissue data from the        at least one diffractometer, transmit the human-tissue data to        the computer database; and process the human-tissue data using a        data analytics algorithm that provides a quantitative-diagnostic        indicator of human tissue.    -   4. The method of paragraph 3, further including the steps of        operatively coupling the at least one tissue diffractometer to a        computer database over a network, and configuring the computer        database for acquisition and transfer of data from the data        group consisting of in situ image data, in situ diffraction        pattern data, and subject data, to the computer database over        the network.    -   5. The method of paragraph 4, further including the steps of        operatively coupling the at least one computer processor to the        at least one tissue diffractometer, and configuring the at least        one computer processor to (i) receive data from the data        group; (ii) transmit data from the data group; and (iii) process        the data from the data group for a human subject using a data        analytics algorithm that provides a quantitative diagnostic        indicator for the human subject.    -   6. The method of paragraph 5, further including the steps of        providing a user interface that allows a human subject's data        from the data group to be uploaded to the computer database in        exchange for processing of the subject's data to receive the        quantitative diagnostic indicator for the subject.    -   7. The system of paragraph 6, further including the steps of        configuring the user interface to allow a human subject or their        healthcare provider to make payments or upload a human subject's        signed consent form.    -   8. The method of paragraph 2, wherein the at least one tissue        diffractometer comprises two or more tissue diffractometers,        each located in different geographic locations.    -   9. The method of paragraph 8, wherein the plural tissue        diffractometers comprise a data encryption device that includes        a global positioning system (GPS) positioning sensor and        generates encrypted data, and the encrypted data transferred to        the computer database track changes in locations of the        plurality of tissue diffractometers.    -   10. The method of paragraph 9, wherein the plural tissue        diffractometers are configured to perform small angle X-ray        scattering (SAXS) measurements.    -   11. The method of paragraph 9, wherein the plural tissue        diffractometers are configured to perform wide angle X-ray        scattering (WAXS) measurements.    -   12. The method of paragraph 9, wherein the plural tissue        diffractometers are further configured to perform mammography.    -   13. The method of paragraph 10, wherein a set of target        coordinates for directing an X-ray beam are determined from a        mammogram.    -   14. The method of paragraph 2, wherein the computer database        resides on a central server.    -   15. The method of paragraph 2, wherein the computer database        resides in the cloud.    -   16. The method of paragraph 4, further including the step of        depersonalizing the data from the data group prior to        transferring it to the computer database.    -   17. The method of paragraph 16, wherein a key for mapping the        depersonalized data stored in the computer database to the human        subject is stored in a local institutional database or in the        human subject's personal files.    -   18. The method of any one of paragraph 5, wherein the data        analytics algorithm comprises a statistical analysis of        diffraction pattern data or a function thereof    -   19. The method of paragraph 18, wherein the statistical analysis        comprises determination of a pair-wise distance distribution        function, determination of a Patterson function, a calculation        of a Porod invariant, a cluster analysis, a dispersion analysis,        determination of one or more molecular structural periodicities,        or any combination thereof.    -   20. The method of paragraph 19, wherein the statistical analysis        comprises a determination of a structural periodicity of        collagen.    -   21. The method of paragraph 19, wherein the statistical analysis        comprises a determination of a structural periodicity of a        lipid.    -   22. The method of paragraph 19, wherein the statistical analysis        comprises a determination of a structural periodicity of a        tissue.    -   23. The method of paragraph 5, wherein the data analytics        algorithm comprises a machine learning algorithm.    -   24. The method of paragraph 23, wherein the machine learning        algorithm comprises a supervised learning algorithm, an        unsupervised learning algorithm, a semi-supervised learning        algorithm, a reinforcement learning algorithm, a deep learning        algorithm, or any combination thereof    -   25. The method of paragraph 24, wherein the machine learning        algorithm is a deep learning algorithm.    -   26. The method of paragraph 25, wherein the deep learning        algorithm is a convolutional neural network, a recurrent neural        network, or a recurrent convolutional neural network.    -   27. The method of paragraph 24, wherein the machine learning        algorithm is trained using a training dataset comprising in situ        image data, in situ diffraction pattern data, subject data, or        any combination thereof stored in the computer database for a        specific pathology or physiological norm group.    -   28. The method of paragraph 27, wherein the training dataset is        updated as new data from the data group.    -   29. The method of paragraph 28, wherein the subject data        comprises a human subject's age, sex, ancestry data, genetic        data, behavioral data, or any combination thereof    -   30. The method of paragraph 29, wherein the quantitative        diagnostic indicator for the human subject comprises an        indicator of the likelihood that the human subject has cancer.    -   31. The method of paragraph 30, wherein the indicator of the        likelihood that the human subject has cancer is an indicator of        the likelihood that the human subject has breast cancer.    -   32. The method of paragraph 30, wherein the quantitative        diagnostic indicator for the human subject comprises a diagnosis        that the human subject has cancer.    -   33. The method of paragraph 32, wherein the diagnosis that the        human subject has cancer is a diagnosis that the human subject        has breast cancer.    -   33. The method of paragraph 2, further comprising repeating the        steps of the method one or more subsequent times to monitor a        disease state of the human subject as the human subject        undergoes a therapeutic treatment.    -   34. The method of paragraph 33, wherein a rate of change of the        disease state of the human subject as indicated by the        quantitative diagnostic indicator provides a measure of the        efficacy of the therapeutic treatment.

While preferred embodiments of the present invention have been shown anddescribed herein, it will be obvious to those skilled in the art thatsuch embodiments are provided by way of example only. It is not intendedthat the invention be limited by the specific examples provided withinthe specification. While the invention has been described with referenceto the aforementioned specification, the descriptions and illustrationsof the embodiments herein are not meant to be construed in a limitingsense. Numerous variations, changes, and substitutions will now occur tothose skilled in the art without departing from the invention.Furthermore, it shall be understood that all aspects of the inventionare not limited to the specific depictions, configurations or relativeproportions set forth herein which depend upon a variety of conditionsand variables. It should be understood that various alternatives to theembodiments of the invention described herein may be employed inpracticing the invention. It is therefore contemplated that theinvention shall also cover any such alternatives, modifications,variations or equivalents. It is intended that the following claimsdefine the scope of the invention and that methods and structures withinthe scope of these claims and their equivalents be covered thereby.

What is claimed:
 1. An in vivo human-tissue analysis and communicationsystem that produces a diagnostic indicator for human tissue analyzed bythe system, comprising: at least one computer processor operativelycoupled to a tissue diffractometer and to a computer database over anetwork, wherein the at least one computer processor is configured foracquisition of human tissue data, wherein the human tissue data ischosen from a data group consisting of in situ image data, in situdiffraction pattern data, and subject data, wherein the in situdiffraction pattern data is acquired from the tissue diffractometer,wherein the at least one computer processor is configured to analyze thehuman tissue data and to produce the diagnostic indicator; and acommunication interface configured to allow the at least one computerprocessor to send and receive information relevant to the diagnosticindicator, wherein the at least one computer processor uses thecommunication interface to receive the human tissue data from the tissuediffractometer and transmit the human tissue data to the computerdatabase over the network.
 2. The system of claim 1, further comprisingthe tissue diffractometer operatively coupled to the computer databaseover the network.
 3. The system of claim 2, wherein the at least onecomputer processor uses the communication interface to process the humantissue data using a data analytics algorithm that provides thediagnostic indicator of the human tissue.
 4. The system of claim 2,wherein the tissue diffractometer is configured for acquisition andtransfer of data from the data group consisting of the in situ imagedata, the in situ diffraction pattern data, and the subject data, to thecomputer database over the network.
 5. The system of claim 4, whereinthe at least one computer processor is configured to: i. receive thedata from the data group; ii. transmit the data from the data group; andiii. process the data from the data group for a human subject using adata analytics algorithm that provides the diagnostic indicator for thehuman tissue.
 6. The system of claim 2, further comprising a userinterface that allows a human subject or their healthcare provider toupload the subject data from the data group to the computer database inexchange for processing of the subject data to receive the diagnosticindicator for the human tissue.
 7. The system of claim 6, wherein theuser interface is further configured to allow the human subject or theirhealthcare provider to make payments or upload a human subject's signedconsent form.
 8. The system of claim 2, comprising at least two of thetissue diffractometers, each being located in different geographiclocations.
 9. The system of claim 2, wherein the tissue diffractometerincludes a data encryption device that includes a global positioningsystem (GPS) positioning sensor and generates encrypted in situ imagedata, encrypted in situ diffraction pattern data, encrypted subjectdata, or any combination thereof, wherein the encrypted in situ imagedata, encrypted in situ diffraction pattern data, encrypted subjectdata, or any combination thereof that is transferred to the computerdatabase tracks changes in location of the tissue diffractometer. 10.The system of claim 9, wherein the tissue diffractometer is configuredto perform small angle X-ray scattering (SAXS) measurements.
 11. Thesystem of claim 10, wherein the tissue diffractometer is configured toperform wide angle X-ray scattering (WAXS) measurements.
 12. The systemof claim 11, wherein the tissue diffractometer is further configured toperform mammography.
 13. The system of claim 10, wherein a set of targetcoordinates for directing an X-ray beam are determined from a mammogram.14. The system of claim 9, wherein the in situ image data, the in situdiffraction pattern data, the subject data, or any combination thereoftransferred to the computer database is depersonalized prior totransfer.
 15. The system of claim 14, wherein a key for mappingdepersonalized in situ image data, depersonalized in situ diffractionpattern data, depersonalized subject data, or any combination thereofstored in the computer database to a human subject is stored in a localinstitutional database or in personal files of the human subject. 16.The system of claim 2, wherein the computer database resides on acentral server.
 17. The system of claim 2, wherein the computer databaseresides in a cloud.
 18. The system of claim 3, wherein the dataanalytics algorithm comprises a statistical analysis of diffractionpattern data or a function thereof.
 19. The system of claim 18, whereinthe statistical analysis comprises determination of a pair-wise distancedistribution function, determination of a Patterson function, acalculation of a Porod invariant, a cluster analysis, a dispersionanalysis, determination of one or more molecular structuralperiodicities, or any combination thereof.
 20. The system of claim 19,wherein the statistical analysis comprises a determination of astructural periodicity of collagen.
 21. The system of claim 19, whereinthe statistical analysis comprises a determination of a structuralperiodicity of one or more lipids.
 22. The system of claim 19, whereinthe statistical analysis comprises a determination of a structuralperiodicity of a tissue.
 23. The system of claim 18, wherein the dataanalytics algorithm comprises a machine learning algorithm.
 24. Thesystem of claim 23, wherein the machine learning algorithm comprises asupervised learning algorithm, an unsupervised learning algorithm, asemi-supervised learning algorithm, a reinforcement learning algorithm,a deep learning algorithm, or any combination thereof.
 25. The system ofclaim 24, wherein the machine learning algorithm is the deep learningalgorithm.
 26. The system of claim 25, wherein the deep learningalgorithm is a convolutional neural network, a recurrent neural network,or a recurrent convolutional neural network.
 27. The system of claim 23,wherein the machine learning algorithm is trained using a trainingdataset comprising image data, diffraction pattern data, the subjectdata, or any combination thereof stored in the computer database for aspecific pathology or physiological norm group.
 28. The system of claim27, wherein the training dataset is updated as new in situ image data,new in situ diffraction pattern data, new subject data, or anycombination thereof and is uploaded to the computer database.
 29. Thesystem of claim 1, wherein the subject data comprises a human subject'sage, sex, ancestry data, genetic data, behavioral data, or anycombination thereof.
 30. The system of claim 29, wherein the diagnosticindicator for the human tissue comprises an indicator of a likelihoodthat the human subject has cancer.
 31. The system of claim 30, whereinthe indicator of the likelihood that the human subject has cancer is anindicator of the likelihood that the human subject has breast cancer.32. The system of claim 29, wherein the diagnostic indicator for thehuman tissue comprises a diagnosis that the human subject has cancer.33. The system of claim 32, wherein the diagnosis that the human subjecthas cancer is a diagnosis that the human subject has breast cancer.